from ..utils import is_flax_available, is_torch_available


if is_torch_available():
					from .autoencoder_kl import AutoencoderKL
					from .controlnet import ControlNetModel
					from .dual_transformer_ad import DualTransformeraDModel
					from .modeling_utils import ModelMixin
					from .prior_transformer import PriorTransformer
					from .ta_film_transformer import TaFilmDecoder
					from .transformer_ad import TransformeraDModel
					from .unet_ad import UNetaDModel
					from .unet_ad import UNetaDModel
					from .unet_ad_condition import UNetaDConditionModel
					from .unet_ad_condition import UNetaDConditionModel
					from .vq_model import VQModel

if is_flax_available():
					from .controlnet_flax import FlaxControlNetModel
					from .unet_ad_condition_flax import FlaxUNetaDConditionModel
					from .vae_flax import FlaxAutoencoderKL


"""simple docstring"""


import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 	__a	(   _lowerCAmelCase     ,  unittest.TestCase						):
       UpperCamelCase_		:       Optional[int]				=      LongformerTokenizer
       UpperCamelCase_		:       str				=      True
       UpperCamelCase_		:       Tuple				=      LongformerTokenizerFast
       UpperCamelCase_		:       List[str]				=      True






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Dict      )->	Any:







              """simple docstring"""
              super().setUp()

              # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
              UpperCamelCase       			=		[
                  "l",
                  "o",
                  "w",
                  "e",
                  "r",
                  "s",
                  "t",
                  "i",
                  "d",
                  "n",
                  "\u0120",
                  "\u0120l",
                  "\u0120n",
                  "\u0120lo",
                  "\u0120low",
                  "er",
                  "\u0120lowest",
                  "\u0120newer",
                  "\u0120wider",
                  "<unk>",
              ]
              UpperCamelCase       			=		dict(zip(UpperCAmelCase_							,   range(len(UpperCAmelCase_      )      )      )      )
              UpperCamelCase       			=		["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
              UpperCamelCase       			=		{"unk_token": "<unk>"}

              UpperCamelCase       			=		os.path.join(self.tmpdirname							,   VOCAB_FILES_NAMES["vocab_file"]      )
              UpperCamelCase       			=		os.path.join(self.tmpdirname							,   VOCAB_FILES_NAMES["merges_file"]      )
              with open(self.vocab_file							,   "w"							,   encoding="utf-8"      ) as fp:
                     fp.write(json.dumps(UpperCAmelCase_      ) + "\n"      )
              with open(self.merges_file							,   "w"							,   encoding="utf-8"      ) as fp:
                     fp.write("\n".join(UpperCAmelCase_      )      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[Any]							,   **UpperCAmelCase_			:				Dict      )->	int:







              """simple docstring"""
              kwargs.update(self.special_tokens_map      )
              return self.tokenizer_class.from_pretrained(self.tmpdirname							,   **UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				str							,   **UpperCAmelCase_			:				Optional[Any]      )->	Optional[int]:







              """simple docstring"""
              kwargs.update(self.special_tokens_map      )
              return self.rust_tokenizer_class.from_pretrained(self.tmpdirname							,   **UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]							,   UpperCAmelCase_			:				Dict      )->	List[Any]:







              """simple docstring"""
              UpperCamelCase       			=		"lower newer"
              UpperCamelCase       			=		"lower newer"
              return input_text, output_text






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple      )->	Optional[Any]:







              """simple docstring"""
              UpperCamelCase       			=		self.tokenizer_class(self.vocab_file							,   self.merges_file							,   **self.special_tokens_map      )
              UpperCamelCase       			=		"lower newer"
              UpperCamelCase       			=		["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
              UpperCamelCase       			=		tokenizer.tokenize(UpperCAmelCase_      )  # , add_prefix_space=True)
              self.assertListEqual(UpperCAmelCase_							,   UpperCAmelCase_      )

              UpperCamelCase       			=		tokens + [tokenizer.unk_token]
              UpperCamelCase       			=		[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
              self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_      )							,   UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	Tuple:







              """simple docstring"""
              UpperCamelCase       			=		self.get_tokenizer()

              self.assertListEqual(tokenizer.encode("Hello world!"							,   add_special_tokens=UpperCAmelCase_      )							,   [0, 31_414, 232, 328, 2]      )
              self.assertListEqual(
                  tokenizer.encode("Hello world! cécé herlolip 418"							,   add_special_tokens=UpperCAmelCase_      )							,   [0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]							,   )






       @slow
       def 						_SCREAMING_SNAKE_CASE							(	self			:				int      )->	int:







              """simple docstring"""
              UpperCamelCase       			=		self.tokenizer_class.from_pretrained("allenai/longformer-base-4096"      )

              UpperCamelCase       			=		tokenizer.encode("sequence builders"							,   add_special_tokens=UpperCAmelCase_      )
              UpperCamelCase       			=		tokenizer.encode("multi-sequence build"							,   add_special_tokens=UpperCAmelCase_      )

              UpperCamelCase       			=		tokenizer.encode(
                  "sequence builders"							,   add_special_tokens=UpperCAmelCase_							,   add_prefix_space=UpperCAmelCase_      )
              UpperCamelCase       			=		tokenizer.encode(
                  "sequence builders"							,   "multi-sequence build"							,   add_special_tokens=UpperCAmelCase_							,   add_prefix_space=UpperCAmelCase_      )

              UpperCamelCase       			=		tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_      )
              UpperCamelCase       			=		tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_							,   UpperCAmelCase_      )

              assert encoded_sentence == encoded_text_from_decode
              assert encoded_pair == encoded_pair_from_decode






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	Optional[Any]:







              """simple docstring"""
              UpperCamelCase       			=		self.get_tokenizer()

              UpperCamelCase       			=		"Encode this sequence."
              UpperCamelCase       			=		tokenizer.byte_encoder[" ".encode("utf-8"      )[0]]

              # Testing encoder arguments
              UpperCamelCase       			=		tokenizer.encode(UpperCAmelCase_							,   add_special_tokens=UpperCAmelCase_							,   add_prefix_space=UpperCAmelCase_      )
              UpperCamelCase       			=		tokenizer.convert_ids_to_tokens(encoded[0]      )[0]
              self.assertNotEqual(UpperCAmelCase_							,   UpperCAmelCase_      )

              UpperCamelCase       			=		tokenizer.encode(UpperCAmelCase_							,   add_special_tokens=UpperCAmelCase_							,   add_prefix_space=UpperCAmelCase_      )
              UpperCamelCase       			=		tokenizer.convert_ids_to_tokens(encoded[0]      )[0]
              self.assertEqual(UpperCAmelCase_							,   UpperCAmelCase_      )

              tokenizer.add_special_tokens({"bos_token": "<s>"}      )
              UpperCamelCase       			=		tokenizer.encode(UpperCAmelCase_							,   add_special_tokens=UpperCAmelCase_      )
              UpperCamelCase       			=		tokenizer.convert_ids_to_tokens(encoded[1]      )[0]
              self.assertNotEqual(UpperCAmelCase_							,   UpperCAmelCase_      )

              # Testing spaces after special tokens
              UpperCamelCase       			=		"<mask>"
              tokenizer.add_special_tokens(
                  {"mask_token": AddedToken(UpperCAmelCase_							,   lstrip=UpperCAmelCase_							,   rstrip=UpperCAmelCase_      )}      )  # mask token has a left space
              UpperCamelCase       			=		tokenizer.convert_tokens_to_ids(UpperCAmelCase_      )

              UpperCamelCase       			=		"Encode <mask> sequence"
              UpperCamelCase       			=		"Encode <mask>sequence"

              UpperCamelCase       			=		tokenizer.encode(UpperCAmelCase_      )
              UpperCamelCase       			=		encoded.index(UpperCAmelCase_      )
              UpperCamelCase       			=		tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]      )[0]
              self.assertEqual(UpperCAmelCase_							,   UpperCAmelCase_      )

              UpperCamelCase       			=		tokenizer.encode(UpperCAmelCase_      )
              UpperCamelCase       			=		encoded.index(UpperCAmelCase_      )
              UpperCamelCase       			=		tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]      )[0]
              self.assertNotEqual(UpperCAmelCase_							,   UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[Any]      )->	List[str]:







              """simple docstring"""
              pass






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	Tuple:







              """simple docstring"""
              for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                     with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"      ):
                            UpperCamelCase       			=		self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_							,   **UpperCAmelCase_      )
                            UpperCamelCase       			=		self.tokenizer_class.from_pretrained(UpperCAmelCase_							,   **UpperCAmelCase_      )
                            UpperCamelCase       			=		"A, <mask> AllenNLP sentence."
                            UpperCamelCase       			=		tokenizer_r.encode_plus(UpperCAmelCase_							,   add_special_tokens=UpperCAmelCase_							,   return_token_type_ids=UpperCAmelCase_      )
                            UpperCamelCase       			=		tokenizer_p.encode_plus(UpperCAmelCase_							,   add_special_tokens=UpperCAmelCase_							,   return_token_type_ids=UpperCAmelCase_      )

                            # token_type_ids should put 0 everywhere
                            self.assertEqual(sum(tokens_r["token_type_ids"]      )							,   sum(tokens_p["token_type_ids"]      )      )

                            # attention_mask should put 1 everywhere, so sum over length should be 1
                            self.assertEqual(
                                sum(tokens_r["attention_mask"]      ) / len(tokens_r["attention_mask"]      )							,   sum(tokens_p["attention_mask"]      ) / len(tokens_p["attention_mask"]      )							,   )

                            UpperCamelCase       			=		tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]      )
                            UpperCamelCase       			=		tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]      )

                            # Rust correctly handles the space before the mask while python doesnt
                            self.assertSequenceEqual(tokens_p["input_ids"]							,   [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]      )
                            self.assertSequenceEqual(tokens_r["input_ids"]							,   [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]      )

                            self.assertSequenceEqual(
                                UpperCAmelCase_							,   ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]      )
                            self.assertSequenceEqual(
                                UpperCAmelCase_							,   ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any      )->	Dict:







              """simple docstring"""
              for trim_offsets, add_prefix_space in itertools.product([True, False]							,   repeat=2      ):
                     UpperCamelCase       			=		self.rust_tokenizer_class.from_pretrained(
                         self.tmpdirname							,   use_fast=UpperCAmelCase_							,   add_prefix_space=UpperCAmelCase_							,   trim_offsets=UpperCAmelCase_      )

                     UpperCamelCase       			=		json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()      )
                     UpperCamelCase       			=		json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()      )

                     self.assertEqual(pre_tokenizer_state["add_prefix_space"]							,   UpperCAmelCase_      )

                     self.assertEqual(post_processor_state["add_prefix_space"]							,   UpperCAmelCase_      )
                     self.assertEqual(post_processor_state["trim_offsets"]							,   UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	Union[str, Any]:







              """simple docstring"""
              # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
              # `trim_offsets`
              for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                     with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"      ):
                            UpperCamelCase       			=		"hello"  # `hello` is a token in the vocabulary of `pretrained_name`
                            UpperCamelCase       			=		f"{text_of_1_token} {text_of_1_token}"

                            UpperCamelCase       			=		self.rust_tokenizer_class.from_pretrained(
                                UpperCAmelCase_							,   use_fast=UpperCAmelCase_							,   add_prefix_space=UpperCAmelCase_							,   trim_offsets=UpperCAmelCase_      )
                            UpperCamelCase       			=		tokenizer_r(UpperCAmelCase_							,   return_offsets_mapping=UpperCAmelCase_							,   add_special_tokens=UpperCAmelCase_      )
                            self.assertEqual(encoding.offset_mapping[0]							,   (0, len(UpperCAmelCase_      ))      )
                            self.assertEqual(
                                encoding.offset_mapping[1]							,   (len(UpperCAmelCase_      ) + 1, len(UpperCAmelCase_      ) + 1 + len(UpperCAmelCase_      ))							,   )

                            UpperCamelCase       			=		self.rust_tokenizer_class.from_pretrained(
                                UpperCAmelCase_							,   use_fast=UpperCAmelCase_							,   add_prefix_space=UpperCAmelCase_							,   trim_offsets=UpperCAmelCase_      )
                            UpperCamelCase       			=		tokenizer_r(UpperCAmelCase_							,   return_offsets_mapping=UpperCAmelCase_							,   add_special_tokens=UpperCAmelCase_      )
                            self.assertEqual(encoding.offset_mapping[0]							,   (0, len(UpperCAmelCase_      ))      )
                            self.assertEqual(
                                encoding.offset_mapping[1]							,   (len(UpperCAmelCase_      ) + 1, len(UpperCAmelCase_      ) + 1 + len(UpperCAmelCase_      ))							,   )

                            UpperCamelCase       			=		self.rust_tokenizer_class.from_pretrained(
                                UpperCAmelCase_							,   use_fast=UpperCAmelCase_							,   add_prefix_space=UpperCAmelCase_							,   trim_offsets=UpperCAmelCase_      )
                            UpperCamelCase       			=		tokenizer_r(UpperCAmelCase_							,   return_offsets_mapping=UpperCAmelCase_							,   add_special_tokens=UpperCAmelCase_      )
                            self.assertEqual(encoding.offset_mapping[0]							,   (0, len(UpperCAmelCase_      ))      )
                            self.assertEqual(
                                encoding.offset_mapping[1]							,   (len(UpperCAmelCase_      ), len(UpperCAmelCase_      ) + 1 + len(UpperCAmelCase_      ))							,   )

                            UpperCamelCase       			=		self.rust_tokenizer_class.from_pretrained(
                                UpperCAmelCase_							,   use_fast=UpperCAmelCase_							,   add_prefix_space=UpperCAmelCase_							,   trim_offsets=UpperCAmelCase_      )
                            UpperCamelCase       			=		tokenizer_r(UpperCAmelCase_							,   return_offsets_mapping=UpperCAmelCase_							,   add_special_tokens=UpperCAmelCase_      )
                            self.assertEqual(encoding.offset_mapping[0]							,   (0, len(UpperCAmelCase_      ))      )
                            self.assertEqual(
                                encoding.offset_mapping[1]							,   (len(UpperCAmelCase_      ), len(UpperCAmelCase_      ) + 1 + len(UpperCAmelCase_      ))							,   )

                            UpperCamelCase       			=		f" {text}"

                            # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                            #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                            # )
                            # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                            # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                            # self.assertEqual(
                            #     encoding.offset_mapping[1],
                            #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                            # )

                            UpperCamelCase       			=		self.rust_tokenizer_class.from_pretrained(
                                UpperCAmelCase_							,   use_fast=UpperCAmelCase_							,   add_prefix_space=UpperCAmelCase_							,   trim_offsets=UpperCAmelCase_      )
                            UpperCamelCase       			=		tokenizer_r(UpperCAmelCase_							,   return_offsets_mapping=UpperCAmelCase_							,   add_special_tokens=UpperCAmelCase_      )
                            self.assertEqual(encoding.offset_mapping[0]							,   (1, 1 + len(UpperCAmelCase_      ))      )
                            self.assertEqual(
                                encoding.offset_mapping[1]							,   (1 + len(UpperCAmelCase_      ) + 1, 1 + len(UpperCAmelCase_      ) + 1 + len(UpperCAmelCase_      ))							,   )

                            UpperCamelCase       			=		self.rust_tokenizer_class.from_pretrained(
                                UpperCAmelCase_							,   use_fast=UpperCAmelCase_							,   add_prefix_space=UpperCAmelCase_							,   trim_offsets=UpperCAmelCase_      )
                            UpperCamelCase       			=		tokenizer_r(UpperCAmelCase_							,   return_offsets_mapping=UpperCAmelCase_							,   add_special_tokens=UpperCAmelCase_      )
                            self.assertEqual(encoding.offset_mapping[0]							,   (0, 1 + len(UpperCAmelCase_      ))      )
                            self.assertEqual(
                                encoding.offset_mapping[1]							,   (1 + len(UpperCAmelCase_      ), 1 + len(UpperCAmelCase_      ) + 1 + len(UpperCAmelCase_      ))							,   )

                            UpperCamelCase       			=		self.rust_tokenizer_class.from_pretrained(
                                UpperCAmelCase_							,   use_fast=UpperCAmelCase_							,   add_prefix_space=UpperCAmelCase_							,   trim_offsets=UpperCAmelCase_      )
                            UpperCamelCase       			=		tokenizer_r(UpperCAmelCase_							,   return_offsets_mapping=UpperCAmelCase_							,   add_special_tokens=UpperCAmelCase_      )
                            self.assertEqual(encoding.offset_mapping[0]							,   (0, 1 + len(UpperCAmelCase_      ))      )
                            self.assertEqual(
                                encoding.offset_mapping[1]							,   (1 + len(UpperCAmelCase_      ), 1 + len(UpperCAmelCase_      ) + 1 + len(UpperCAmelCase_      ))							,   )





